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SUSPENSION SIMULATION MODEL VERIFICATION THROUGH EXPERIMENTAL DATA FOR LATERAL FORCE IN FIGURE-8 TESTING

Rabun Wallace

rabun_z_wallace@GeorgiaSouthern.edu Graduate Student, Mechanical Engineering Department, Georgia Southern University

Bikiran Guha

bikiran_guha@GeorgiaSouthern.edu Graduate Student, Electrical Engineering Department, Georgia Southern University

Michael Santangelo

michael_p_santangelo@GeorgiaSouthern.edu Undergraduate Student, Electrical Engineering Department, Georgia Southern University

Aniruddha Mitra, PhD, PE mitra@georgiasouthern.edu

Associate Professor, Mechanical Engineering Department, Georgia Southern University

Rami Haddad, PhD rhaddad@georgiasouthern.edu

Assistant Professor, Electrical Engineering Department, Georgia Southern University Abstract: Research is being done on vehicles and their suspensions to find ways to increase the safety of the operators and passengers by maintaining the cars ability to maintain control and stability. The objective of this work is to increase the stability of an off-road vehicle under harsh driving conditions. The current research is focused on developing a simulation model of a semi-active suspension for such vehicles. A Mini-Baja car that was built and raced by the Georgia Southern Mechanical Engineering students was used for the experimental verification of the simulation model. The parameters for the Mini-Baja were used to create a CarSim simulation model. A Honeywell 6-way IMU was mounted on the actual Mini-Baja to measure the accelerations as well as the rotational movement of the car around all three axes. The IMU was interfaced with an Arduino microcontroller using a CAN bus to record all the measurements attained during the vehicle’s operations. In addition, a GPS unit was used to track the GPS coordinates of the vehicle’s path and record the speed of the vehicle. Also, a LCD display was used to display the speed of the car during operation for the driver. Tests were simulated at a set speed and then the results were compared with experimental data and necessary modifications were made to the simulation model. This work will be used to help in future research which can be applied to full size vehicles and ATVs or UTVs for safer operations. Keywords: CarSim, Honeywell IMU, Lateral Acceleration, Model Verification, Introduction: The automotive industry is very important for engineers as there are many different aspects that required extensive research and design to ensure the safety and increase the performance of automobiles for the consumers. One system that is being highly research in recent years is the suspension system as it is responsible for the handling and stability of the car. Increasing the

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stability of the car allows for better handling by maintaining maximum contact for the tires on the road. It also allows for better handling by reducing tire slip through the maximum contact with the ground on all four corners. The suspension system of automobiles can take multiple forms and configurations depending on the needs and requirements of the vehicle to perform under certain circumstances or conditions. The independent suspensions are most often duel control arm or multi-link systems which allow for each corner of the car’s suspension to move freely without affecting one another so that each tire can remain in contact with the ground during extreme driving situations. Each corner of an independent suspension has springs and dampeners built in to absorb external forces on the car and increase rider comfort as well as allow for the most stable ride possible. The spring will act to return the body to its equilibrium position and the dampeners will absorb shock and vibrations in the suspension due to external forces. These components are very important to reduce vehicle roll angle and maintain control of the car. The Society of Automotive Engineers, SAE, holds events for colleges were student teams are able to design, build, and compete with Mini-Baja racecars in off-road competitions on a national level. These events are hosted by schools throughout the United States and test the ability of the Baja cars in speed, agility, toughness and strength of design, as well as ability to overcome various terrains. Each school spends the school year assembling its team for designing and manufacturing the different subsystems to comply with the rules listed by the SAE Baja rule book and are required to use a 10 horsepower Briggs and Stratton engine. This is meant to increase the competitiveness of the races and to base the cars ability off of the designs of the students instead of monetary advantages. Literature Review: Active and Semi-active suspensions have been used for many years to increase stability and handling of all types of vehicles [1]. Bose, Daimler Chrysler, Land Rover, and Delphi have used these types of systems either as factory or aftermarket applications successfully. The further research that this has led to more research for their use. Controlling or aiding in stabilizing a car’s roll, pitch, and yaw through the suspension would be especially beneficial for vehicles with a high center of gravity such as SUVs, trucks, and ATV. In active and semi-active suspensions intended to be use for anti-rolling, the roll angle of the car is the main parameter that is controlled. The dominant contribution effecting rollover is the lateral force which acts on the inner and outer tires. As it exceeds levels which the car is designed to withstand, rollover conditions begins to occur [2][3][4]. This threshold is generally set as 4-10 degrees for the roll angle because it is found that wheel lift-off is commonly found to occur near 23 degrees of roll [2]. Common tests used for testing vehicles for rollover conditions are the double-lane change and fish-hook manoeuvers which are both exert high amounts of lateral forces on the tires [2][3][4]. Variables used to assess the rollover conditions are the rollover coefficient, rolling angle, lateral accelerations, and time to rollover [2][3]. Testing in part by Ford Motor Company found rolling failure to occur at 45 km/hr with peak forces and rate around 1 second during a J-turn configuration experiment [5]. Baris Aykent showed in a paper that the use of a simple hydraulic actuator to generate a warning through a controller and increase the soll stiffness of the anti-rolling bar at the point where the roll angle is nearing the threshold for failure can aid in stabilizing the vehicle [2]. Time to rollover, TTR, is taken to reach maximum at 4.62 seconds. Another test used semi-active hydro-pneumatic springs and dampers on each wheel to control the

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stability and rolling [3]. Other papers have also shown the use of differential braking to prevent rollover [4][6][7]. Simulations have been proven to be a very useful tool for accessing the abilities of dynamic rollover prevention. Equations for the yaw, roll, linear accelerations, or other numerical data can be solved by complex models in ADAMS, Simulink [3], and CarSim [4] for many aspects of a vehicle’s suspension. These programs can be very helpful to better understand the effects of alterations to a suspension as well as to increase the research knowledge base through better comprehension of the forces in different driving conditions. One test showed the use of a CMM, coordinate measuring machine, to find specifications which could then be input into ADAMS Car software to model the vehicle and the program was used to make alterations which allowed for better understanding of the effects of the different configurations [8]. Another journal used MATLAB/Simulink to test and design an electronic system for electrical cars through the use of different formulas and algorithms that represented different components and found the forces attained in each member. They also used this method to reduce unstable conditions by controlling the braking and suspension roll [9]. Further literature showed where ADAMS was used along with MATLAB/Simulink to generate accurate and effective simulations of suspensions [10]. Using the two programs simultaneously is shown to be very effective for solving problems through the extended mathematical models that MATLAB/Simulink is capable of implementing for individual blocks then they can be applied to the program. One paper discussed the use of a hydraulic system with controllers that measured the forces in the system during a bump or turn and applied corrective forces to negate the effects of the road on the body and suspension. This system used 13 sensors to measure conditions on the vehicle. By using check valves and a controller the hydraulic fluid in the component that replaced the shock on the suspension system would be forced into the needed chambers or reservoirs to compensate for the applied force. The constant pressure that the system kept on the shock like apparatus allowed for a smoother ride better control of the car. This also was able to reduce the roll angle by 0.5 degrees during the simulations from standard suspension structures. This type of system could be modified to apply it to all types of vehicles by altering the size of the part and editing the controller used to counteract the forces applied during operations. Data Acquisition Interface: An inertial measurement unit (IMU) from Honeywell was selected to validate the data from the simulation software. The IMU has six degrees of freedom in measurement, i.e., it can measure translational movement in three orthogonal axes and rotational movement about three orthogonal axes. The IMU can output eight different parameters, namely roll angle, pitch angle, roll rate, pitch rate, yaw rate, longitudinal, lateral, and vertical acceleration. Table I lists the range, accuracy and frequency response of the IMU for each type of parameter. It is equipped with a Controller Area Network (CAN) bus to communicate with microcontrollers or other devices [1]. The CAN bus follows the 29-bit SAEJ1939 CAN serial communication protocol. An Arduino UNO board was selected as the microcontroller to convert the binary data from the IMU to human-readable measurements. However, the Arduino UNO board does not have the ability to communicate with the IMU. Therefore, it was augmented with a DEV-10039 CAN-BUS shield from Sparkfun which uses a MCP2515 CAN controller and a MCP2551 CAN transceiver to communicate with the CAN bus in the IMU. The CAN-BUS shield had the added capability to interface with a GPS receiver from Sparkfun and this functionality was utilized to measure the speed of the vehicle. The whole setup was powered by a 12V battery.

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Table 1: IMU Sensor Specifications

Instrumentation: To properly record data from the IMU and GPS module each piece of hardware, and the power supply was fixed to the mini Baja vehicle. Several steps were taken to mount and fixture the data acquisition hardware to the chassis of the mini Baja vehicle. The IMU was mounted on the bottom plane of the frame, to a plate that was welded to the frame. The IMU was mounted in a location that was close to the true center of gravity of the vehicle, longitudinally laterally and vertically, but the final location for the device was approximately 6” to the left of the center line of the vehicle, due to the need for a correct position vertically and longitudinally. Unfortunately the CarSIM software cannot relocate the location from which it measures lateral acceleration, but the next iteration of the vehicle dynamics model will be made in Adams/Car, which has this ability. The IMU was mounted on the bottom plane of the frame, to a plate that was welded to the frame. Due to the fact that the center of gravity of the vehicle is the longitudinal centerline of the vehicle, the IMU was mounted as close to this line as possible, but was offset by ~6 in. due to the seating position of the driver. In addition to securely fastening the hardware, supplementary precautions had to be taken to reduce vibrational noise from distorting the signals read by the IMU. Neoprene rubber washers were used to add a layer of cushioning between the chassis of the mini Baja and the IMU itself. Because captive nuts were used to fasten the IMU to the plate, distributing the torque between the three mounting locations played a key part in removing static measurement error from the IMU readings. A digital angle finder was placed on the same plane that the IMU was mounted to and then zeroed out. Then it was placed on top the IMU and each fastener was torqued so that the deflection was 0°, ensuring the IMU was parallel with the mini Baja frame. The GPS module, and Arduino used for recording the IMU measurements were mounted inside a Dribox™ weatherproof electrical enclosure. The enclosure was then fastened to the under tray of the mini Baja through the use of custom bent U-clamps.

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Figure 1: Block Diagram of the Instrumentation System

Calibration: Testing of accuracy of the IMU and GPS module was using several static and dynamic methods. To the test IMU’s roll angle readings it was fixed to an adjustable angle plate for a vertical milling machine. The angle plate rests on the milling table and can be adjusted to +/- 45°. Using 1-2-3 blocks and finger clamps the IMU was fixed to prevent movement during tilting of the plate. Figure 2 shows the IMU mounted to the angle plate.

Figure 2: IMU Attached to Adjustable Angle Plate

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Once securely fastened, the angle plate was moved from 0 to -45°, in increments of 5°. Readings from the IMU were taken at every increment, and the same was done from 0 to 45°. The readings showed a steady state error of ~1°. The GPS module accuracy was tested dynamically. For a rough estimate of accuracy the GPS was compared against a GPS app for a smart phone. It was observed that because of the quicker refresh rate that the GPS bounced between values more, but the moving average for speed was within 1-2 mph of the GPS app. Next the GPS module was tested during normal operation of the mini Baja vehicle. The speed of the car was measured over the course of a 25 minute driving session, and the compared to simulated maximum speed and to calculated theoretical maximum speed of the vehicle. Based on gearing of the vehicle, the computed top speed is equal to 32.6 mph. Simulation data for top speed is based on available horsepower of the engine, and factors in drag losses, bearing and transmission efficiency, and tire characteristics. The maximum speed from the simulation is ~39 mph. This shows that the vehicle will reach its top speed based on gearing before it runs out of horsepower. During the 25 minute sessions the top speed of the vehicle was reached on multiple occasions during a long downhill straight-away section. The maximum measured value of speed from the GPS module was 31 mph. This is a difference of 1.6 mph, or approximately 5%. This error was also taken into account when post processing the data from the GPS module. Testing: To test the lateral acceleration of the mini Baja vehicle, a single circular turn test was run. The test consists of running 5 to 10 laps around a 35 ft. radius circle. This distance was chosen because it is close to the stock simulation test that CarSim has, which is a 10 meter radius circle. The distance of the turn is also large enough for the driver to maintain a steady speed and steering angle without the need to adjust, which would happen if the turn was too tight to maintain by the vehicles suspension and steering systems. Another consideration for testing was the elevation change of the pavement. Of all the possible accessible locations for testing, the flattest zone was chosen, a paved parking lot located on a flat section of terrain. The parking lot also offered a high amount of grip for the tires, eliminating the chance that the tire could slip under the drive torque. To further reduce error in the measurements, the data acquisition systems were turned on prior to driving the circular turn test and steady state error was measured and accounted for. Simulation Model Verification: In this project, CarSim software was used to create a model of the FSAE Mini-Baja racecar for testing and verification under circular course conditions. The generic model for the Mini-Baja car was used as the base then it was modified to mimic Georgia Southern University’s vehicle which was built by the mechanical engineering students and used to compete in the 2014 FSAE national competitions. Modifications to the general characteristics were based off of the designs used for the actual car. For the overall characteristics of the vehicle, the weight was modified to be 281.18 kg, the wheel base to be 1.8796 meters, and the track width front and back was altered to 1.2954 meters and 1.1049 meters respectively. Also, the weight bias was modified to 49 % front and 51 % rear which are the measured specifications of the car. The height of the center of gravity for the car is designed to be 635 mm above the ground and it is assumed to be along the center line of the car. To find the center of gravity location for the distance front to back of the

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car, the weight bias is multiplied to the wheel base length and the value of 51% rear bias times the wheelbase 1.524 meters gives a value of 777.24 mm from the front axle. Other parameters adjusted for the vehicle were there kingpin inclination angle of 12.5 degrees and an offset of 50.8 mm. The static toe was set to be 0 degrees in the front and -1 degree out in the rear. The static caster angle was set to 15 degrees in the front and 0 degrees in the rear. The static camber is specified to be -2 degrees in the front and -0.5 degrees in the rear. The suspension system uses Fox Float R shocks which have duel chambers that are designed to act as both springs and shocks. Using the graphical data from Fox Pro, the front rate is set to be 4.378 N and the rear is set to be 17.51 N. The suspension was designed to have 203 mm of jounce and 101 mm of rebound. The tires for the car are 22x7x10 inch tires so the radius of the tires is calculated to be 279.4 mm. These are some of the more important changes that were made to allow for the simulation to match the actual vehicle as closely as possible. The simulations were run on a figure 8 skid pad course. The course has a radius of 9.125 meters for each side of the course. Friction for the pavement and tires is set at 1.0 to maintain traction without slipping like the actual experiments. During the simulation, the car enters the course in the center where the two circles meet and proceeds around the circle to the right twice then goes through the second loop twice, making two right-hand turns loops and two left-hand loops, then exits out of the circles in the opposite direction from where it entered. The simulations were run first at a constant speed of 20 kph which simulates the lower speed experiments. Since the second set of experiments was slightly faster, a set of simulations were run at 25 kph to compare to the average speed for those experiments. As well, CarSim has a circular course with a radius 30ft which was used as a second validation for the model to the actual vehicle. Similarly to the figure-8 course simulations, tests were run first at 20 kph then at 25 kph and the roll angle and lateral acceleration was compared to experimental data for both the course configurations. Hand Calculations: Hand calculations were also done to solve for the lateral accelerations of the experiments from data recorded during the tests. Using the known diameter of the circular course set up during testing and the time needed to complete a set number of laps, the speed average of the laps was calculated by dividing the distance traveled around the circumference by the time. The lateral acceleration can be solved for by using the formula . The results were then divided by 9.81 m/s2 to calculate for the forces in gravitational forces known as g forces. Results: The IMU attained the data for the lateral acceleration which we used to validate the simulation model through comparison of the results. The first set of data in the graph was from running the course at an average of 20 KPH. The results are given in m/s2 which shows the average to be approximately 3.5 m/s2 which converts to 0.357 g. Where the data peaks at the end of the run, the vehicle was sped up slightly during the last two laps. The next set of data on the graph is from an increased speed of 25 kph and the course was diven 4 laps counterclockwise then the driver exited the circle and reentered running in a clockwise direction. The last set was another test running clockwise around the course. The increased speed tests resulted in the lateral acceleration increasing to an average of 5.5 m/s2 counter clockwise and 5.1 m/ss clockwise in both tests. Figure 3 shows these results.

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Figure 3: Experimental Lateral Acceleration Results from the IMU

The simulations for the circular course found the lateral acceleration to be approximately 0.35 G for the case run at 20 KPH and the roll angle was shown to be 6.3 degrees. The set of simulations run on the Figure-8 course showed similar results with the peak lateral acceleration in the 20 KPH approximately 0.37 G and the roll angle around to be 6.7 degrees. Figure 4 shows a comparison of the experimental results to the simulations for the circular and Figure-8 courses for the lateral acceleration. The roll angle results for the courses are shown in Figure 5 and 6 respectively. The hand calculation for the lateral acceleration at 20 KPH is 0.3996 G. After increasing the speed to 25 KPH, the simulation showed the lateral acceleration to be approximately 0.56 G and the hand calculations gave a value of 0.71 G for the circular course. The roll angle in the higher speed test was found to be approximately 10 degrees. In the Figure-8 course, increasing the speed to 25 KPH, the lateral acceleration increased to 0.59 G and the roll angle increased to be approximately 10 degrees on average. Figure 7 shows the comparison of the experiments run at 25 KPH to the simulations for both courses at this speed. As well, the roll angle results are shown in Figures 8 and 9. The negative results shown for the Figure-8 simulations are a result of the clockwise turns which were run first before running in a counter clockwise direction.

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Figure 4: Lateral Acceleration of 20 kph Experimental and Simulated Results

Figure 5: Roll Angle for Circular Figure 6: Roll Angle for Figure-8,

Course, 20 kph 20 kph

Figure 7: Lateral Acceleration of 25 kph Experimental and Simulated Results

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Figure 8: Roll Angle for Circular Course, Figure 9: Roll Angle for Figure-8, 25 kph 25 kph Discussion: In the experimental data, the results for the lateral acceleration show an average of approximately 0.357 g and in the results from the simulations the average result is approximately 0.35 g which gives a difference of 1.9%. This shows that the results from the simulation match closely to the experimental results. When the speed was increased to 25 kph, the results for the experiment average as 5.5 m/s2 which is equal to 0.56 g in comparison to the data from the simulation which gave the acceleration to be 0.71 g. The higher speed tests on the circular course gave and error of 21.1% when comparing the results to the counterclockwise results and 26.7% when compared to the clockwise experimental results. There are multiple causes attributing to the increased error. In the experimental results it can be seen that the data has many variation. These are due to noise from the engine running and vibrations through driving conditions as well as many other factors that are uncontrollable in experimentation. One condition that affected the results for the experiment regarding driving conditions was that the testing sight had a small slope and was not perfectly level. The tests for simulations base the results off of ideal conditions so these type factors are not shown in the results. As well, there are variations from the results for clockwise and counterclockwise experiments. One main contributor to this difference is the fact that the IMU was able to be placed exactly in the center of the vehicle. This distance that it is off of the center of gravity location causes the data to be slightly skewed by not allowing the rotation center to be at the origin point for the IMU. In the figure-8 course simulation results we find that the lateral acceleration is very similar to the circular course results. The 20 kph simulations attained a gravitational force of 0.37 g while the 25 kph simulation had an increase in the force to 0.59 g. This gives an error for the two simulations at 3.6 % for the 20 kph simulation and 5.4% and 13.5% respectively for the counterclockwise and clockwise data. The results attained in the simulations match closely with the experimental results which validates the model. The roll angle results in the simulations are given as 6.3 degree for the lower speed simulation and 10 degree for the higher speed simulations. These results can be compared to the data found in the literature and seen to be close to the expected values. Conclusion: The validation of the simulation model is necessary to attaining accurate results for experimentation with it in the future. Data acquisition through the IMU allowed for this validation by being able to compare the results to the simulation model to ensure the accuracy of the values being attained. As well using mathematical calculations solved by hand for the lateral acceleration, further validated the results attained by both the simulation software and the experiment. With the model being validated for the lateral acceleration, other data attain during simulations will be considered accurate as well. This allows for a base line value to be used for future experimentation from the simulation results for other data as well as the lateral

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acceleration. The base level results being attained for these two course types show a consistent forces acting on the car through the lateral acceleration in the turns causing the vehicle to roll slightly. In future research, the application of Matlab/Simulink in the CarSim simulation model will allow for better understanding of the ability of a modified semi-active suspension to increase the stability and reduce the roll of the car during similar maneuvers. Also, simulations will be run with off-road courses that will contain various types of harsh conditions which will likely be encountered during the FSAE Mini-Baja events. Conditions like potholes, banked corners, and non-level ground can cause higher rollover concern through sudden changes in the travel of the car. Simulations with the semi-active suspension will be able to increase knowledge of the effect these forces have on the suspension system and better ways to counteract them for increased control and stability. The research for the FSAE Mini-Baja can help advance the knowledge for other teams in racing applications to increase safety and performance. As well it can be used in other high center of gravity applications that are similar to it such as ATVs and UTVs, which are used by many for working purposes as well as recreational use. By increasing the stability of these types of these smaller vehicles, application of similar systems on other vehicles for both military and civilian purposes will enable the safety of operation and allow for increased ability of the vehicles to perform under extreme conditions in both on and off-road procedures. References: [1] Hubert, K., & Kumar, A. (2005). Anti-Roll Stability Suspension Technology. Papers-

Society Of Automotive Engineers New York, (3522), ALL. [2] Aykent, B. (2010). Effects of Sway Acceleration Control on Rollover Propensity and

Assessment of Lateral Specific Forces (No. 2010-01-1325). SAE Technical Paper. [3] van der Westhuizen, S. F., & Els, P. S. (2013). Slow active suspension control for

rollover prevention. Journal of Terramechanics, 50(1), 29-36. [4] Ungoren, A. Y., & Peng, H. (2004). Evaluation of vehicle dynamic control for rolover

prevention. International Journal of Automotive Technology, 5(2), 115-122. [5] McCoy, R. W., Chou, C. C., van de Velde, R., Twisk, D., & van Schie, C. (2007).

Vehicle rollover sensor test modeling (No. 2007-01-0686). SAE Technical Paper. [6] Chen, B. C., & Peng, H. (2000, August). Rollover prevention for sports utility vehicles

with human-in-the-loop evaluations. In 5th Int’l Symposium on Advanced Vehicle Control.

[7] Wielenga, T. J., & Chace, M. A. (2000). A study in rollover prevention using anti-rollover braking (No. 2000-01-1642). SAE Technical Paper.

[8] Rao, P. S., Roccaforte, D., Campbell, R., & Zhou, H. (2002). Developing an ADAMS® Model of an Automobile Using Test Data (No. 2002-01-1567). SAE Technical Paper.

[9] Peng, J., He, H., & Feng, N. (2013). Simulation research on an electric vehicle chassis system based on a collaborative control system. Energies, 6(1), 312-328.

[10] Fei, L., ZhaoXiang, D., Hongliang, D., & Youqiang, C. (2007). The Co-simulation of Steering and Suspension System with ADAMS&Matlab/Simulink (No. 2007-01-3666). SAE Technical Paper.

[11] Hanxing Chen, Jun Tian, "Research on the Controller Area Network," International Conference on Networking and Digital Society, 2009, vol.2, no., pp.251,254, 30-31 May 2009

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Biography: Rabun Wallace is a graduate student at Georgia Southern University and is pursuing a degree in Applied Engineering with a focus in Energy Science in the Mechanical Engineering department. He grew up working in a family used car dealership where he worked as a mechanic, as well as worked on a family cattle farm. These aided in increasing ability and interest in engineering as he worked to repair and make items frequently as well as to look for better designs ergonomics and strength. With a drive to figure out how things work and what makes them function, mechanical engineering was the degree of choice to pursue as he graduated high school and applied for college. His undergraduate degree was in Mechanical Engineering

Technology at Georgia Southern University and graduated in 2011. While working on his bachelor’s degree, he worked on research for the fuel efficiency of different mixtures of diesel and bio-diesel fuels. As well, he worked as the propulsion team leader for the design and building of an underwater remotely operated vehicle. After the undergraduate degree, he worked at a small firearms manufacture in Georgia for nearly 2 years in which the first half of the time was spent working as a manufacturing engineer to aid in certain production lines and then was moved to work in the quality department as a quality engineer for different product lines. In the fall of 2013 he returned back to Georgia Southern University where he is currently working on his Master’s degree. He is working on thesis research on the effects of water particle size into diesel combustion through the use of computational fluid dynamic software. Also, he worked as the mechanical engineering student doing modeling and analysis of data for simulation of a FSAE Mini Baja racecar using CarSim, which is being published and presented at the Vibrations Institute Training Conference. He is scheduled to graduate in December and is looking to work in the design and research for the performance automotive industry.